Senior Data Informatics Manager – AI/ML Engineering Development

KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world's leading technology providers to accelerate the delivery of tomorrow's electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.

The Information Technology (IT) group at KLA is involved in every aspect of the global business. IT's mission is to enable business growth and productivity by connecting people, process, and technology. It focuses not only on enhancing the technology that enables our business to thrive but also on how employees use and are empowered by technology. This integrated approach to customer service, creativity and technological excellence enables employee productivity, business analytics, and process excellence.

Job Description/Preferred Qualifications

Senior Data Informatics Manager - AI/ML Engineering Development

We are seeking a highly accomplishedSenior Data Informatics Managerto lead the strategy, development, and delivery of advancedAI/ML engineering solutionsthat enable data-driven decision-making, intelligent automation, and operational excellence across the enterprise. This leader will play a critical role in shaping and scaling solutions spanningadvanced analytics, machine learning, Generative AI, NLP, large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI frameworks.

The ideal candidate brings a strong blend oftechnical depth, applied innovation, and business partnership, with a track record of delivering high-impact solutions in areas such as predictive analytics, workflow automation, quality intelligence, system intelligence, conversational AI, and enterprise data science.

Key Responsibilities

• Lead the vision, roadmap, and execution of enterpriseAI/ML and data informatics initiativesthat improve operational efficiency, decision support, and service quality.
• Drive the design, development, and deployment of advanced analytical solutions usingclassification, clustering, regression, time series analysis, neural networks, and statistical modeling.
• Oversee the development ofGenerative AI and NLP-based solutions, including applications involving summarization, text mining, sentiment analysis, automation, and LLM-enabled workflows.
• Lead the creation ofcontext-aware conversational systems, includingRAG-enabled assistants, chatbot platforms, and knowledge-driven AI applications.
• Guide the implementation ofmulti-agent and agentic AI systemsto automate complex workflows, improve orchestration, and enhance enterprise decision-making.
• Partner closely with cross-functional stakeholders across engineering, operations, product, cybersecurity, and business teams to identify high-value opportunities and translate them into scalable AI/ML solutions.
• Establish and promote best practices across the AI/ML lifecycle, including data preparation, model development, validation, deployment readiness, performance monitoring, and continuous improvement.
• Mentor and develop high-performing teams of data scientists, ML engineers, and informatics professionals, fostering technical excellence, innovation, and business impact.
• Communicate technical strategy, solution value, and execution progress effectively to senior leaders and diverse stakeholder groups.

Required Qualifications

• Master's degree or higher inData Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative disciplinewith research focus indata mining, machine learning, text mining, databases, statistics, and forecasting.
• 5+ years of progressive experiencein data science, analytics, machine learning, or informatics, including experience delivering enterprise-scale solutions.
• Demonstrated expertise inmachine learning and statistical methods, including classification, clustering, regression, time series analysis, hypothesis testing, Bayesian analysis, and neural networks.
• Strong experience withGenerative AI technologies, includingLLMs, OpenAI tools, NLP techniques, LangChain, LangGraph, AutoGen, swarm or multi-agent frameworks, and vector database-based architectures.
• Proven experience building or leading solutions involvingRAG, advanced chatbots, knowledge-aware AI systems, and intelligent workflow automation.
• Expertise intext analytics, summarization, sentiment analysis, and unstructured data processingfor real-world business applications.
• Proficiency with modern analytics and ML development tools such asPython, R, SAS, TensorFlow, PyTorch, SQL, and data visualization platforms.
• Strong communication and leadership capabilities, with the ability to influence technical and business stakeholders and convert complex challenges into scalable solutions.

Preferred Qualifications

• Experience leading teams or mentoring technical professionals indata science, AI/ML engineering, or data informaticsenvironments.
• Hands-on experience withLLM-powered automation, systems intelligence workflows, or agentic AI.
• Demonstrated ability to quickly adopt emerging technologies and apply them effectively to create innovative, production-relevant solutions.

Minimum Qualifications

• Master's degree or higher inData Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative disciplinewith research focus indata mining, machine learning, text mining, databases, statistics, and forecasting.
• 5+ years of progressive experiencein data science, analytics, machine learning, or informatics, including experience delivering enterprise-scale solutions.
• Demonstrated expertise inmachine learning and statistical methods, including classification, clustering, regression, time series analysis, hypothesis testing, Bayesian analysis, and neural networks.
• Strong experience withGenerative AI technologies, includingLLMs, OpenAI tools, NLP techniques, LangChain, LangGraph, AutoGen, swarm or multi-agent frameworks, and vector database-based architectures.

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